computing the weight of LDA topic for all the documents in the corpus

Question

I computed my LDA model, I retrieved my topics and now I am looking for the way to compute the weight/percentage of each topic on the corpus. Surprisingly I cannot find the way to do this, so far my code looks like:

## Libraries to download
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim import corpora, models
import gensim

## Tokenizing
tokenizer = RegexpTokenizer(r'\w+')

# create English stop words list
en_stop = stopwords.words('english')

# Create p_stemmer of class PorterStemmer
p_stemmer = PorterStemmer()

import json
import nltk
import re
import pandas

appended_data = []

#for i in range(20014,2016):
#    df0 = pandas.DataFrame([json.loads(l) for l in open('SDM_%d.json' % i)])
#    appended_data.append(df0)

for i in range(2005,2016):
    if i > 2013:
        df0 = pandas.DataFrame([json.loads(l) for l in open('SDM_%d.json' % i)])
        appended_data.append(df0)
    df1 = pandas.DataFrame([json.loads(l) for l in open('Scot_%d.json' % i)])
    df2 = pandas.DataFrame([json.loads(l) for l in open('APJ_%d.json' % i)])
    df3 = pandas.DataFrame([json.loads(l) for l in open('TH500_%d.json' % i)])
    df4 = pandas.DataFrame([json.loads(l) for l in open('DRSM_%d.json' % i)])
    appended_data.append(df1)
    appended_data.append(df2)
    appended_data.append(df3)
    appended_data.append(df4)


appended_data = pandas.concat(appended_data)
# doc_set = df1.body

doc_set = appended_data.body

# list for tokenized documents in loop
texts = []

# loop through document list
for i in doc_set:

    # clean and tokenize document string
    raw = i.lower()
    tokens = tokenizer.tokenize(raw)

    # remove stop words from tokens
    stopped_tokens = [i for i in tokens if not i in en_stop]

    # add tokens to list
    texts.append(stopped_tokens)

# turn our tokenized documents into a id <-> term dictionary
dictionary = corpora.Dictionary(texts)

# convert tokenized documents into a document-term matrix
corpus = [dictionary.doc2bow(text) for text in texts]

# generate LDA model
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=15, id2word = dictionary, passes=50)
ldamodel.save("model.lda0")

So far, what I have seen in other forums is to do the following:

from itertools import chain
print(type(doc_set))
print(len(doc_set))

for top in ldamodel.print_topics():
  print(top)
print

# Assinging the topics to the document in corpus
lda_corpus = ldamodel[corpus]
#print(lda_corpus)

# Find the threshold, let's set the threshold to be 1/#clusters,
# To prove that the threshold is sane, we average the sum of all probabilities:
scores = list(chain(*[[score for topic_id,score in topic] \
                     for topic in [doc for doc in lda_corpus]]))
print(sum(scores))
print(len(scores))
threshold = sum(scores)/len(scores)
print(threshold)

cluster1 = [j for i,j in zip(lda_corpus,doc_set) if i[0][1] > threshold]
cluster2 = [j for i,j in zip(lda_corpus,doc_set) if i[1][1] > threshold]
cluster3 = [j for i,j in zip(lda_corpus,doc_set) if i[2][1] > threshold]

However I get the error in the cluster two: IndexError: list index out of range. Any idea why?

Economist_Ayahuasca · Accepted Answer · 2016-05-31T18:53:41.870

4

You need to state a minimum probability to zero in the lda function:

ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=15, id2word = dictionary, passes=50, minimum_probability=0)

Moreover, you can just get the topic-distribution for all articles by:

for i in range(len(doc_set)):
    print(ldamodel[corpus[i]])

edited May 31 '16 at 18:53

answered May 30 '16 at 16:52

Economist_Ayahuasca

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computing the weight of LDA topic for all the documents in the corpus

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